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Background: We designed a deep learning model for assessing 18 F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer., Methods: All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result., Results: In total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively., Conclusion: This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients., Key Points: • This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. • All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. • For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.

Purpose: The aim of the study was to investigate the predictive role of pretreatment metabolic tumor volume (MTV) in patients with squamous cell carcinoma of locally advanced esophageal cancer treated with definitive chemoradiotherapy., Patients and Methods: Ninety patients received pretreatment with (18)F-fluorodeoxyglucose ((18)F-FDG) PET/CT, and two types of MTVs were measured on the basis of either a maximal standardized uptake value (SUVmax) of 2.5 (MTV2.5) or a fixed threshold of 20% (MTV20%). Overall survival (OS) and disease-free survival (DFS) were examined, and independent prognosticators were identified by Cox regression analysis., Results: On a median follow-up of 15 months, 51 patients were seen to have died because of tumor recurrence or other illnesses. Multivariate analysis of OS revealed that MTV20%>40 ml was the only predictor of outcome with a lower 1-year OS [P=0.003, hazard ratio (HR)=2.29, 95% confidence interval (CI) 1.36-3.91]. Two independent predictors of DFS were MTV20%>40 ml (P=0.02, HR=1.78, 95% CI 1.09-2.91) and stage IV disease (P=0.01, HR=1.84, 95% CI 1.12-3.03)., Conclusion: Pretreatment MTV20% is a novel marker for OS and DFS in patients with unresectable locally advanced esophageal cancer treated with definitive chemoradiotherapy. Treatment intensification must be considered for patients with higher MTVs.

Background: This study examined genomic factors associated with a reduction in fluoro-2-deoxy- D-glucose (FDG) uptake during positron emission tomography-computed tomography (PET-CT) for definitive chemoradiotherapy (CRT) in patients with pharyngeal cancer. Methods: The pretreatment and interim PET-CT images of 25 patients with advanced pharyngeal cancers receiving definitive CRT were prospectively evaluated. The maximum standardized uptake value (SUV) of the interim PET-CT and the reduction ratio of the SUV (SRR) between the two images were measured. Genomic data from pretreatment incisional biopsy specimens ( SLC2A1, CAIX, VEGF, HIF1A, BCL2, Claudin-4, YAP1, MET, MKI67, and EGFR) were analyzed using tissue microarrays. Differences in FDG uptake and SRRs between tumors with low and high gene expression were examined using the Mann-Whitney test. Cox regression analysis was performed to examine the effects of variables on local control. Results: The SRR of the primary tumors (SRR-P) was 0.59 ± 0.31, whereas the SRR of metastatic lymph nodes (SRR-N) was 0.54 ± 0.32. Overexpression of HIF1A was associated with a high iSUV of the primary tumor ( P < 0.001) and neck lymph node ( P = 0.04) and a low SRR-P ( P = 0.02). Multivariate analysis revealed that patients who had tumors with low SRR-P or high HIF1A expression levels showed inferior local control. Conclusion: In patients with pharyngeal cancer requiring CRT, HIF1A overexpression was positively associated with high interim SUV or a slow reduction in FDG uptake. Prospective trials are needed to determine whether the local control rate can be stratified using the HIF1A level as a biomarker and SRR-P. [ABSTRACT FROM AUTHOR]

Objectives/Hypothesis The study aimed to investigate the prognostic effects of interim 18fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) during definitive radiotherapy (RT) or chemoradiotherapy (CRT) in patients with head and neck cancer. Study Design This is a prospectively treatment study. Methods The pretreatment and interim PET/CT images of 51 patients with advanced pharyngeal cancers receiving definitive RT/CRT were evaluated prospectively. The interim PET/CT images were taken at a cumulative RT dose ranging from 41.4 to 46.8 Gy. The maximum standardized uptake value (SUVm) of the interim PET/CT and the reduction ratio of the SUVm (SRR) between the two images were measured. The differences between patients with or without local failures were examined using the Mann-Whitney test. Overall survival (OS), disease-free survival (DFS), and primary and nodal relapse-free survival rates were calculated using the Kaplan-Meier method. Independent prognosticators were identified using Cox regression analysis. Results After a median follow-up duration of 23 months, a higher interim SUVm was associated with local failures. Conducting a multivariate analysis revealed that a SUVmax reduction ratio of primary tumor (SRR-P) < 0.64 was associated with the inferior OS (hazard ratio 2.64; P =0.035) and DFS (hazard ratio 2.33; P = 0.045). Patients who had tumors with an SRR-P < 0.64 had a considerably lower 2-year OS and DFS compared with those who had SRR-P ≥ 0.64 (47% vs. 66%; 41% vs. 64%). Conclusion A higher interim SUVm was associated with local recurrence. In addition, patients with a lower SRR-P should be considered to be at risk of primary failure. Level of Evidence 4. Laryngoscope, 124:2732-2738, 2014 [ABSTRACT FROM AUTHOR]

Simple Summary: Neoadjuvant chemoradiotherapy (NCRT) before surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model was introduced to predict responses to NCRT. The model employed metric learning combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. Using this model, the area under the receiver operating characteristic curve was 0.96 for predicting a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. After further external validation, oncologists may use the proposed model to advise patients on the relative suitability of treatment options, including the therapeutic decision between NCRT and neoadjuvant chemotherapy. Integrating this approach would have a notable effect on counseling patients about treatment alternatives or prognoses. Objectives: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT. Patients and Methods: This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. Results: In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively. Conclusions: The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model's predictive value. [ABSTRACT FROM AUTHOR]